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Configuring the workflow

Running the workflow requires configuring three files: config.yaml, samples.tsv, and units.tsv. config.yaml is used to configure the analyses, samples.tsv categorizes your samples into groups, and units.tsv connects sample names to their input data files. The workflow will use config/config.yaml automatically, but it can be good to name it something informative and point to it when running snakemake with --configfile <path>.

samples.tsv

This file contains your sample list, and has four tab separated columns:

sample	population	time	depth
hist1	Hjelmseryd	historical	low
hist2	Hjelmseryd	historical	low
hist3	Hjelmseryd	historical	low
mod1	Gotafors	modern	high
mod2	Gotafors	modern	high
mod3	Gotafors	modern	high

  • sample contains the ID of a sample. It is best if it only contains alphanumeric characters.

  • population contains the population the sample comes from and will be used to group samples for population-level analyses.

  • time sets whether a sample should be treated as fresh DNA or historical DNA in the sequence processing workflow. Doesn't change anything if you're starting with bam files.

  • depth puts the sample in a sequencing depth category. Used for filtering - if enabled in the configuration, extreme depth filters will be performed for depth categories individually.

units.tsv

This file connects your samples to input files and has a potential for eight tab separated columns:

sample	unit	lib	platform	fq1	fq2	bam	sra
hist1	BHVN22DSX2.2	hist1	ILLUMINA	data/fastq/hist1.r1.fastq.gz	data/fastq/hist1.r2.fastq.gz
hist1	BHVN22DSX2.3	hist1	ILLUMINA	data/fastq/hist1.unit2.r1.fastq.gz	data/fastq/hist1.unit2.r2.fastq.gz
hist2	BHVN22DSX2.2	hist2	ILLUMINA	data/fastq/hist2.r1.fastq.gz	data/fastq/hist2.r2.fastq.gz
hist3	BHVN22DSX2.2	hist2	ILLUMINA	data/fastq/hist3.r1.fastq.gz	data/fastq/hist3.r2.fastq.gz
mod1	AHW5NGDSX2.3	mod1	ILLUMINA	data/fastq/mod1.r1.fastq.gz	data/fastq/mod1.r2.fastq.gz
mod2	AHW5NGDSX2.3	mod2	ILLUMINA			data/bam/mod2.bam
mod3	AHW5NGDSX2.3	mod3	ILLUMINA	data/fastq/mod3.r1.fastq.gz	data/fastq/mod3.r2.fastq.gz
SAMN13218652	SRR10398077	SAMN13218652	ILLUMINA				SRR10398077
  • sample contains the ID of a sample. Must be same as in samples.tsv and may be listed multiple times when inputting multiple sequencing runs/libraries.
  • unit contains the sequencing unit, i.e. the sequencing lane barcode and lane number. This is used in the PU and (part of) the ID read groups. If you don't have multiple sequencing lanes per samples, this won't impact anything. Doesn't do anything when using bam input.
  • lib contains the name of the library identifier for the entry. Fills in the LB and (part of) the ID read groups and is used for PCR duplicate removal. Best practice would be to have the combination of unit and lib to be unique per line. An easy way to use this is to use the Illumina library identifier or another unique library identifier, or simply combine a generic name with the sample name (sample1A, sample1B, etc.). Doesn't do anything when using bam input.
  • platform is used to fill the PL read group. Commonly is just 'ILLUMINA'. Doesn't do anything when using bam input.
  • fq1 and fq2 provides the absolute or relative to the working directory paths to the raw sequencing files corresponding to the metadata in the previous columns.
  • bam provides the absolute or relative to the working directory path of pre-processed bam files. Only one bam files should be provided per sample in the units file.
  • sra provides the NCBI SRA accession number for a set of paired end fastq files that will be downloaded to be processed. If a sample has multiple runs you would like to include, each run should be its own line in the units sheet, just as separate sequencing runs would be.

Mixing samples with different starting points

It is possible to have different samples start from different inputs (i.e. some from bam, others from fastq, others from SRA). It is best to provide only fq1+fq2, bam, or sra for a single sample to be clear where that sample starts. If multiple starts are provided for the same sample, the bam file will override fastq or SRA entries, and the fastq will override SRA entries. Note that this means it is not currently possible to have multiple starting points for the same sample (i.e. FASTQ reads that would be processed then merged into an existing BAM).

Configuration file

config.yaml contains the configuration for the workflow, this is where you will put what analyses, filters, and options you want. Below I describe the configuration options. The config.yaml in this repository serves as a template and includes some 'default' parameters that may be good starting points for many users. If --configfile is not specified in the snakemake command, the workflow will default to config/config.yaml.

Configuration options

Dataset Configuration

Required configuration of the 'dataset'.

  • samples: An absolute or relative path from the working directory to the samples.tsv file.
  • units: An absolute or relative path from the working directory to the units.tsv file.
  • dataset: A name for this dataset run - essentially, an identifier for a batch of samples to be analysed together with the same configuration.

It is best to name your dataset something descriptive, but concise. This is because this name will be used in organizing the results. Outputs of analyses will be placed in the folder results/{dataset}, and files will be prefaced with the dataset. This allows for multiple datasets to be run in the same working directory, even in parallel (if they aren't trying to make the same files), which is useful for multi-species projects or for testing out different filters. You can simply have a config for each dataset and choose which one to run with --configfile. A similar approach can be used to trying out different analysis parameters.

Example use of multiple datasets

Say you want to run PopGLen on two sets of samples, but use the same reference. You can have two sample lists: dataset1_samples.tsv and dataset2_samples.tsv, and two config files: dataset1_config.tsv and dataset2_config.yaml. In the configs, the samples: option should point to the corresponding sample list. The workflow for dataset1 can be run, if you pass --configfile config/dataset1_config.yaml to Snakemake, then the same can be done for dataset2. However, when dataset2 is run, it will use any outputs from dataset1 it can, such as reference indices, reference filters, etc. Additionally, if the two datasets share samples, those samples will not have to be remapped for dataset2, they'll be taken from the dataset1 run. The actual analyses are partitioned by dataset, into the folders results/dataset1 and results/dataset2.

Reference Configuration

Required configuration of the reference.

  • chunk_size: A size in bp (integer). Your reference will be analyzed in 'chunks' of contigs of this size to parallelize processing. This size should be larger than the largest contig in your genome. A larger number means fewer jobs that run longer. A smaller number means more jobs that run shorter. The best fit will depend on the reference and the compute resources you have available. Leaving this blank will not divide the reference up into chunks (but this isn't optimized yet, so it will do a couple unnecessary steps).

  • reference:

  • name: A name for your reference genome, will go in the file names.

  • fasta: A path to the reference fasta file (currently only supports uncompressed fasta files)
  • mito: Mitochondrial contig name(s), will be removed from analysis. Should be listed within brackets []
  • sex-linked: Sex-linked contig name(s), will be removed from analysis. Should be listed within brackets []
  • exclude: Additional contig name(s) to exclude from analysis. Should be listed within brackets []

  • min_size: A size in bp (integer). All contigs below this size will be excluded from analysis.

  • ancestral: A path to a fasta file containing the ancestral states in your reference genome. This is optional, and is used to polarize allele frequencies in SAF files to ancestral/derived. If you leave this empty, the reference genome itself will be used as ancestral, and you should be sure the [params] [realSFS] [fold] is set to 1. If you put a fasta here, you can set that to 0.

Reference genomes should be uncompressed, and contig names should be clear and concise. Currently, there are some issues parsing contig names with underscores, so please change these in your reference before running the pipeline. Alphanumeric characters, as well as . in contig names have been tested to work so far, other symbols have not been tested.

Potentially the ability to use bgzipped genomes will be added, I just need to check that it works with all underlying tools. Currently, it will for sure not work, as calculating chunks is hard-coded to work on an uncompressed genome.

Sample Set Configuration

  • exclude_ind: Sample name(s) that will be excluded from the workflow. Should be a list in []. Putting a # in front of the sample in the sample list also works. Mainly used to drop samples with poor quality after initial processing.
  • excl_pca-admix: Sample name(s) that will be excluded only from PCA and Admixture analyses. Useful for close relatives that violate the assumptions of these analyses, but that you want in others. Should be a list in []. If you want relatives out of all downstream analyses, not just PCA/Admix, put them in exclude_ind instead. Note this will trigger a re-run for relatedness analyses, but you can just disable them now as they've already been run.

Analysis Selection

Here, you will define which analyses you will perform. It is useful to start with only a few, and add more in subsequent workflow runs, just to ensure you catch errors before you use compute time running all analyses. Most are set with (true/false) or a value, described below. Modifications to the settings for each analysis are set in the next section.

  • populations: A list of populations found in your sample list to limit population analyses to. Might be useful if you want to perform individual analyses on some samples but not include them in any population level analyses. Leave blank ([]) if you want population level analyses on all the populations defined in your samples.tsv file.

  • analyses:

  • mapping:
    • historical_only_collapsed: Historical samples are expected to have fragmented DNA. For this reason, overlapping (i.e. shorter, usually <270bp) read pairs are collapsed in this workflow for historical samples. Setting this option to true will only map only these collapsed reads, and is recommended to target primarily endogenous content. However, in the event you want to map both the collapsed and uncollapsed reads, you can set this to false. (true/false)
    • historical_collapsed_aligner: Aligner used to map collapsed historical sample reads. aln is the recommended for this, but this is here in case you would like to select mem for this. Uncollapsed historical reads will be mapped with mem if historical_only_collapsed is set to false, regardless of what is put here. (aln/mem)
  • pileup-mappability: Filter out sites with low 'pileup mappability', which describes how uniquely fragments of a given size can map to the reference (true/false)
  • repeatmasker: (NOTE: Only one of the four options should be filled/true)
    • bed: Supply a path to a bed file that contains regions with repeats. This is for those who want to filter out repetitive content, but don't need to run Repeatmodeler or masker in the workflow because it has already been done for the genome you're using. Be sure the contig names in the bed file match those in the reference supplied. GFF or other filetypes that work with bedtools subtract may also work, but haven't been tested.
    • local_lib: Filter repeats by masking with an already made library you have locally (such as ones downloaded for Darwin Tree of Life genomes). Should be file path, not a URL.
    • dfam_lib: Filter repeats using a library available from dfam. Should be a taxonomic group name.
    • build_lib: Use RepeatModeler to build a library of repeats from the reference itself, then filter them from analysis (true/false).
  • extreme_depth: Filter out sites with extremely high or low global sequencing depth. Set the parameters for this filtering in the params section of the yaml. (true/false)
  • dataset_missing_data: A floating point value between 0 and 1. Sites with data for fewer than this proportion of individuals across the whole dataset will be filtered out in all analyses using the filtered sites file. (This is only needed if you need to ensure all your analyses are using exactly the same sites, which I find may result in coverage biases in results, especially heterozygosity. Unless you explicitly need to ensure all groups and analyses use the same sites, I would leave this blank, instead using the [params][angsd][minind_dataset] to set a minimum individual threshold for dataset level analyses, allowing analyses to maximize sites per group/sample. This is how most papers do it.)
  • population_missing_data: A floating point value between 0 and 1. Sites with data for fewer than this proportion of individuals in any population will be filtered out in all populations using the filtered sites file. (This is only needed if you need to ensure all your populations are using exactly the same sites, which I find may result in coverage biases in results, especially heterozygosity. Unless you explicitly need to ensure all groups and analyses use the same sites, I would leave this blank, instead using the [params][angsd][minind_pop] to set a minimum individual threshold for each analyses, allowing analyses to maximize sites per group/sample. This is how most papers do it.)
  • qualimap: Perform Qualimap bamqc on bam files for general quality stats (true/false)
  • ibs_ref_bias: Enable reference bias calculation. For each sample, one read is randomly sampled at each position and compared to the reference base. These are summarized as the proportion of the genome that is identical by state to the reference for each sample to quantify reference bias. This is done for all filter sets as well as for all sites without site filtering. If transition removal or other arguments are passed to ANGSD, they are included here. (true/false)
  • damageprofiler: Estimate post-mortem DNA damage on historical samples with Damageprofiler (true/false) NOTE: This just adds the addition of Damageprofiler to the already default output of MapDamage.
  • mapdamage_rescale: Rescale base quality scores using MapDamage2 to help account for post-mortem damage in analyses (if you only want to assess damage, use damageprofiler instead, they return the same results) (true/false) docs
  • estimate_ld: Estimate pairwise linkage disquilibrium between sites with ngsLD for each popualation and the whole dataset. Note, only set this if you want to generate the LD estimates for use in downstream analyses outside this workflow. Other analyses within this workflow that require LD estimates (LD decay/pruning) will function properly regardless of the setting here. (true/false)
  • ld_decay: Use ngsLD to plot LD decay curves for each population and for the dataset as a whole (true/false)
  • pca_pcangsd: Perform Principal Component Analysis with PCAngsd. Currently requires at least 4 samples to finish, as it will by default try to plot PCs1-4. (true/false)
  • admix_ngsadmix: Perform admixture analysis with NGSadmix (true/false)
  • relatedness: Relatedness is estimated using two methods: IBSrelate (Waples et al. 2019, Mol. Ecol.) and NgsRelate v2 (Hanghøj et al. 2019; GigaScience). IBSrelate does not require allele frequencies, which is useful if you do not have sufficient sample size to confidently estimate allele frequencies for your populations. In this pipeline, it is can be run three ways: using the (1) IBS and (2) SFS based methods described in the Waples paper using ANGSD or (3) using the SFS based method's implementation in NgsRelate v2 (which still does not require allele frequencies). NgsRelate v2 also offers an allele frequency based method, which enables co-inference of inbreeding and relatedness coefficients. If using this method, PopGLen will calculate the allele frequencies for your populations and input them into NgsRelate. These different methods have trade-offs in memory usage and run time. Generally, I recommend starting with the NgsRelate, using IBSrelate only (ngsrelate_ibsrelate-only), using the other approaches as you need them.
    • ibsrelate_ibs: Estimate pairwise relatedness with the IBS based method from Waples et al. 2019, Mol. Ecol.. This can use a lot of memory, as it has genotype likelihoods for all sites from all samples loaded into memory, so it is done per 'chunk', which still takes a lot of time and memory. NOTE: For those removing transitions, this method does not include transition removal. All other relatedness methods here do. (true/false)
    • ibsrelate_sfs: Estimate pairwise relatedness with the SFS based method from Waples et al. 2019, Mol. Ecol.. Enabling this can greatly increase the time needed to build the workflow DAG if you have many samples. As a form of this method is implemented in NGSrelate, it may be more efficient to only enable that. (true/false)
    • ngsrelate_ibsrelate-only: Performs the IBSrelate SFS method, but on SNPs using NgsRelate. Does not need to estimate allele frequencies. (true/false)
    • ngsrelate_freqbased: Performs the allele frequency based co-inference of relatedness and inbreeding that NgsRelate is primarly intended for. Will estimate allele frequencies per population and use them in the analysis. Also runs the IBSrelate SFS method in NgsRelate. (true/false)
  • 1dsfs: Generates a one dimensional site frequency spectrum for all populations in the sample list. Automatically enabled if thetas_angsd is enabled. (true/false)
  • 1dsfs_boot: Generates N bootstrap replicates of the 1D site frequency spectrum for each population. N is determined from the sfsboot setting below (true/false)
  • 2dsfs: Generates a two dimensional site frequency spectrum for all unique populations pairings in the sample list. Automatically enabled if fst_angsd is enabled. (true/false)
  • 2dsfs_boot: Generates N bootstrap replicates of the 2D site frequency spectrum for each population pair. N is determined from the sfsboot setting below (true/false)
  • thetas_angsd: Estimate pi, theta, and Tajima's D for each population in windows across the genome using ANGSD (true/false)
  • heterozygosity_angsd: Estimate individual genome-wide heterozygosity using ANGSD. Calculates confidence intervals from bootstraps. (true/false)
  • fst_angsd: Estimate pairwise $F_{ST}$ using ANGSD. Set one or both of the below options. Estimates both globally and in windows across the genome.
    • populations: Pairwise $F_{ST}$ is calculated between all possible population pairs (true/false)
    • individuals: Pairwise $F_{ST}$ is calculated between all possible population pairs. NOTE: This can be really intensive on the DAG building process, so I don't recommend enabling unless you're certain you want this (true/false)
  • inbreeding_ngsf-hmm: Estimates inbreeding coefficients and runs of homozygosity using ngsF-HMM. Output is converted into an inbreeding measure $F_ROH$, which describes the proportion of the genome in runs of homozygosity over a certain length. (true/false)
  • ibs_matrix: Estimate pairwise identity by state distance between all samples using ANGSD. (true/false)
  • pop_allele_freqs: Estimates population-specific minor allele frequencies for each population in the dataset using ANGSD. Two outputs are generated per population: (1) population-specific minor allele frequencies, where only sites variable in the population are included and the minor allele is set to the minor of the population, and (2) dataset-wide minor allele frequencies, where the minor allele is set to the minor of the entire dataset and includes sites that are fixed within the population if they are variable in the dataset.

Subsampling Section

As this workflow is aimed at low coverage samples, its likely there might be considerable variance in sample depth. For this reason, it may be good to subsample all your samples to a similar depth to examine if variation in depth is influencing results. To do this, set an integer value here to subsample all your samples down to and run specific analyses. This subsampling can be done in reference to the unfiltered sequencing depth, the mapping and base quality filtered sequencing depth, or the filtered sites sequencing depth. The latter is recommended, as this will ensure that sequencing depth is made uniform at the analysis stage, as it is these filtered sites that analyses are performed for.

  • subsample_dp: A list of mean depths to subsample your reads to. This will be done per sample, and subsample from all the reads. Leaving list empty disables subsampling, list can contain any number of depths to subsample to. If a sample already has the same, or lower, depth than this number, it will just be used as is in the analysis. (List of INT)
  • subsample_by: This determines how the 'full' sequencing depth of a sample is calculated to determine the amount of subsampling needed to reach the target depth. This should be one of three options: (1) "unfilt" will treat the sequencing depth of all (unfiltered) reads and sites as the 'full' depth; (2) "mapqbaseq" will filter out reads that don't pass the configured mapping or base quality, then calculate depth across all sites as the 'full' depth, (3) "sitefilt" will filter reads justa as "mapqbaseq" does, but will only calculate the 'full' depth from sites passing the sites filter. As the main goal of subsampling is to make depth uniform for analyses, this latter option is preferred, as it will most accurately bring the depth of the samples to the target depth for analyses. ("unfilt"/"mapqbaseq"/"sitefilt")
  • redo_depth_filts: If subsample_by is set to "unfilt" or "mapqbaseq", then it would be possible to recaculate extreme depth filters for the subsampled dataset. Enable this to do so, otherwise, the depth filters from the full depth bams will be used. If subsample_by is set to "sitefilt" this will have no effect, as the subsampling is already in reference to a set site list. (true/false)
  • drop_samples: When performing depth subsampling, you may want to leave some samples out that you kept in your 'full' dataset. These can be listed here and they will be removed from ALL depth subsampled analyses. A use case for this might be if you have a couple samples that are below your targeted subsample depth, and you don't want to include them. Note that if you configure multiple subsample_dp, these samples will be dropped from all of them. If you need to perform mutliple depth subsamplings with different subsets of samples, its best to run each depth individually. Alternatively, a config file can be made for each subsampled depth, however you may run into issues of file locking blocking both from running at the same time. (list of strings: [])
  • subsample_analyses: Individually enable analyses to be performed with the subsampled data. These are the same as the ones above in the analyses section. Enabling here will only run the analysis for the subsampled data, if you want to run it for the full data as well, you need to enable it in the analyses section as well. (true/false)

Filter Sets

By default, this workflow will perform all analyses requested in the above section on all sites that pass the filters set in the above section. These outputs will contain allsites-filts in the filename and in the report. However, many times, it is useful to perform an analysis on different subsets of sites, for instance, to compare results for genic vs. intergenic regions, neutral sites, exons vs. introns, etc. Here, users can set an arbitrary number of additional filters using BED files. For each BED file supplied, the contents will be intersected with the sites passing the filters set in the above section, and all analyses will be performed additionally using those sites.

For instance, given a BED file containing putatively neutral sites, one could set the following:

filter_beds:
  neutral-sites: "resources/neutral_sites.bed"

In this case, for each requested analysis, in addition to the allsites-filts output, a neutral-filts (named after the key assigned to the BED file in config.yaml) output will also be generated, containing the results for sites within the specified BED file that passed any set filters.

More than one BED file can be set, up to an arbitrary number:

filter_beds:
  neutral: "resources/neutral_sites.bed"
  intergenic: "resources/intergenic_sites.bed"
  introns: "resources/introns.bed"

It may also sometimes be desireable to skip analyses on allsites-filts, say if you are trying to only generate diversity estimates or generate SFS for a set of neutral sites you supply.

To skip running any analyses for allsites-filts and only perform them for the BED files you supply, you can set only_filter_beds: true in the config file. This may also be useful in the event you have a set of already filtered sites, and want to run the workflow on those, ignoring any of the built in filter options by setting them to false.

Software Configuration

This section contains the specific settings for each software, allowing users to customize the settings used. The default configuration file contains settings that are commonly used, and should be applicable to most datasets sequenced on patterened flow cells, but please check that they make sense for your analysis. If you are missing a configurable setting you need, open up an issue or a pull request and I'll gladly put it in if possible.

Note to historical sample users wanting to remove transitions

While most the defaults below are good for most datasets, including ones with historical samples and using MapDamage rescaling, transition removal is turned off by default. To enable transition removal to account for post-mortem DNA damage, enable the option rmtrans in the angsd section below. This will fill in the appropriate flag -rmTrans or -noTrans depending on the analysis, and remove transitions from all analyses. Only the IBS based IBSrelate method currently does not support transition removal.

  • mapQ: Phred-scaled mapping quality filter. Reads below this threshold will be filtered out. (integer)

  • baseQ: Phred-scaled base quality filter. Reads below this threshold will be filtered out. (integer)

  • params:

  • clipoverlap:
    • clip_user_provided_bams: Determines whether overlapping read pairs will be clipped in BAM files supplied by users. This is useful as many variant callers will account for overlapping reads in their processing, but ANGSD will double count overlapping reads. If BAMs were prepped without this in mind, it can be good to apply before running through ANGSD. However, it essentially creates a BAM file of nearly equal size for every sample, so it may be nice to turn off if you don't care for this correction or have already applied it on the BAMs you supply. (true/false)
  • genmap: Parameters for pileup mappability analysis, see GenMap's documentation for more details.
    • K:
    • E:
    • map_thresh: A threshold mappability score. Each site gets an average mappability score taken by averaging the mappability of all K-mers that would overlap it. A score of 1 means all K-mers are uniquely mappable, allowing for e mismatches. This is doen via a custom script, and may eventually be replaced by the SNPable method, which is more common. (integer/float, 0-1)
  • extreme_depth_filt: Parameters for excluding sites based on extreme high and/or low global depth. The final sites list will contain only sites that pass the filters for all categories requested (i.e the whole dataset and/or the depth categories set in samples.tsv).
    • method: Whether you will generate extreme thresholds as a multiple of the median global depth ("median") or as percentiles of the global depth distribution ("percentile")
    • bounds: The bounds of the depth cutoff, defined as a numeric list. For the median method, the values will be multiplied by the median of the distribution to set the thresholds (i.e. [0.5,1.5] would generate a lower threshold at 0.5*median and an upper at 1.5*median). For the percentile method, these define the lower and upper percentiles to filter out (i.e [0.01,0.99] would remove the lower and upper 1% of the depth distributions). ([ FLOAT, FLOAT])
    • filt-on-dataset: Whether to perform this filter on the dataset as a whole (may want to set to false if your dataset global depth distribution is multi-modal). (true/false)
    • filt-on-depth-classes: Whether to perform this filter on the depth classes defined in the samples.tsv file. This will generate a global depth distribution for samples in the same category, and perform the filtering on these distributions independently. Then, the sites that pass for all the classes will be included. (true/false)
  • fastp:
    • extra: Additional options to pass to fastp trimming. (string)
    • min_overlap_hist: Minimum overlap to collapse historical reads. Default in fastp is 30. This effectively overrides the --length_required option if it is larger than that. (INT)
  • bwa_aln:
    • extra: Additional options to pass to bwa aln for mapping of historical sample reads. (string)
  • samtools:
    • subsampling_seed: Seed to use when subsampling bams to lower depth. "$RANDOM" can be used to set a random seed, or any integer can be used to set a consistent seed. (string or int)
  • picard:
    • MarkDuplicates: Additional options to pass to Picard MarkDuplicates. --REMOVE_DUPLICATES true is recommended. (string)
  • angsd: General options in ANGSD, relevant doc pages are linked
    • gl_model: Genotype likelihood model to use in calculation (-GL option in ANGSD, docs)
    • maxdepth: When calculating individual depth, sites with depth higher than this will be binned to this value. Should be fine for most to leave at 1000. (integer, docs)
    • mindepthind: Individuals with sequencing depth below this value at a position will be treated as having no data at that position by ANGSD. ANGSD defaults to 1 for this. Note that this can be separately set for individual heterozygosity estimates with mindepthind_heterozygosity below. (integer, -setMinDepthInd option in ANGSD) (INT)
    • minind_dataset: Used to fill the -minInd option for any dataset wide ANGSD outputs (like Beagles for PCA/Admix). Should be a floating point value between 0 and 1 describing what proportion of the dataset must have data at a site to include it in the output. (FLOAT)
    • minind_pop: Used to fill the -minInd option for any population level ANGSD outputs (like SAFs or Beagles for ngsF-HMM). Should be a floating point value between 0 and 1 describing what proportion of the population must have data at a site to include it in the output. (FLOAT)
    • rmtrans: Removes transitions using ANGSD, effectively removing them from downstream analyses. This is useful for removing DNA damage from analyses, and will automatically set the appropriate ANGSD flags (i.e. using -noTrans 1 for SAF files and -rmTrans 1 for Beagle files.) (true/false)
    • extra: Additional options to pass to ANGSD during genotype likelihood calculation at all times. This is primarily useful for adding BAM input filters. Note that --remove_bads and -only_proper_pairs are enabled by default, so they only need to be included if you want to turn them off or explicitly ensure they are enabled. I've also found that for some datasets, -C 50 and -baq 1 can create a strong relationship between sample depth and detected diversity, effectively removing the benefits of ANGSD for low/variable depth data. I recommend that these aren't included unless you know you need them. Since the workflow uses bwa to map, -uniqueOnly 1 doesn't do anything if your minimum mapping quality is > 0. Mapping and base quality thresholds are also not needed, it will use the ones defined above automatically. If you prefer to correct for historical damage by trimming the ends of reads, this is where you'd want to put -trim INT. (string) (string, docs)
    • extra_saf: Same as extra, but only used when making SAF files (used for SFS, thetas, Fst, IBSrelate, heterozygosity includes invariable sites). Doesn't require options already in extra or defined via other params in the YAML (such as notrans, minind, GL, etc.) (string)
    • extra_beagle: Same as extra, but only used when making Beagle and Maf files (used for PCA, Admix, ngsF-HMM, doIBS, ngsrelate, includes only variable sites). Doesn't require options already in extra or defined via other params in the YAML (such as rmtrans, minind, GL, etc.) (string)
    • snp_pval: The p-value to use for calling SNPs (float, docs) (float or string)
    • domajorminor: Method for inferring the major and minor alleles. Set to 1 to infer from the genotype likelihoods, see documentation for other options. 1, 2, and 4 can be set without any additional configuration. 5 must also have an ancestral reference provided in the config, otherwise it will be the same as 4. 3 is currently not possible, and is used for generating the dataset minor allele MAFs. If you have a use for 3 that PopGLen is suited for, please open an issue and I will look into it. (int)
    • domaf: Method for inferring minor allele frequencies. Set to 1 to infer from genotype likelihoods using a known major and minor from the domajorminor setting above. Set to 2 to assume the minor is unknown. See docs for more information. 3 is possible, and will estimate the frequencies both assuming a known and unknown minor. If you choose this option, you'll get both in the MAF outputs, but only the known will be passed to NgsRelate if you also use that. Other values are currently unsupported in PopGLen. (int)
    • min_maf: The minimum minor allele frequency required to call a SNP. This is set when generating the beagle file, so will filter SNPs for PCAngsd, NGSadmix, ngsF-HMM, and NGSrelate. If you would like each tool to handle filtering for maf on its own you can set this to -1 (disabled). (float, docs)
    • mindepthind_heterozygosity: When estimating individual heterozygosity, sites with sequencing depth lower than this value will be dropped. (integer, -setMinDepthInd option in ANGSD) (int)
  • ngsld: Settings for ngsLD (docs)
    • max_kb_dist_est-ld: For the LD estimates generated when setting estimate_ld: true above, set the maximum distance between sites in kb that LD will be estimated for (--max_kb_dist in ngsLD, integer)
    • rnd_sample_est-ld: For the LD estimates generated when setting estimate_ld: true above, randomly sample this proportion of pairwise linkage estimates rather than estimating all (--rnd_sample in ngsLD, float)
    • max_kb_dist_decay: The same as max_kb_dist_est-ld:, but used when estimating LD decay when setting ld_decay: true above (integer)
    • rnd_sample_decay: The same as rnd_sample_est-ld:, but used when estimating LD decay when setting ld_decay: true above (float)
    • fit_LDdecay_extra: Additional plotting arguments to pass to fit_LDdecay.R when estimating LD decay (string)
    • fit_LDdecay_n_correction: When estimating LD decay, should the sample size corrected r^2 model be used? (true/false, true is the equivalent of passing a sample size to fit_LDdecay.R in ngsLD using --n_ind)
    • max_kb_dist_pruning_dataset: The same as max_kb_dist_est-ld:, but used when linkage pruning SNPs as inputs for PCAngsd, NGSadmix, and NGSrelate analyses. Pruning is performed on the whole dataset. Any positions above this distance will be assumed to be in linkage equilibrium during the pruning process. (integer)
    • pruning_min-weight_dataset: The minimum r^2 to assume two positions are in linkage disequilibrium when pruning for PCAngsd, NGSadmix, and NGSrelate analyses. (float)
  • ngsrelate: Settings for NGSrelate
    • ibsrelate-only-extra: Any extra command line parameters to be passed to NgsRelate when performing an IBSrelate only (no allele frequencies) run. (string)
    • freqbased-extra: Any extra command line parameters to be passed to NgsRelate when performing a standard, allele frequency based, run. (string)
  • ngsf-hmm: Settings for ngsF-HMM
    • estimate_in_pops: Set to true to run ngsF-HMM separately for each population in your dataset. Set to false to run for whole dataset at once. ngsF-HMM assumes Hardy-Weinberg Equilibrium (aside from inbreeding) in the input data, so select the option that most reflects this in your data. (true/false)
    • prune: Whether or not to prune SNPs for LD before running the analysis. ngsF-HMM assumes independent sites, so it is preferred to set this to true to satisfy that expectation. (true/false)
    • max_kb_dist_pruning_pop: The maximum distance between sites in kb that will be treated as in LD when pruning for the ngsF-HMM input. (INT)
    • pruning_min-weight_pop: The minimum r^2 to assume two positions are in linkage disequilibrium when pruning for the ngsF-HMM input. Note, that this likely will be substantially higher for individual populations than for the whole dataset, as background LD should be higher when no substructure is present. (float)
    • min_roh_length: Minimum ROH size in base pairs to include in inbreeding coefficient calculation. Set if short ROH might be considered low confidence for your data. (integer)
    • roh_bins: A list of integers that describe the size classes in base pairs you would like to partition the inbreeding coefficient by. This can help visualize how much of the coefficient comes from ROH of certain size classes (and thus, ages). List should be in ascending order and the first entry should be greater than min_roh_length. The first bin will group ROH between min_roh_length and the first entry, subsequent bins will group ROH with sizes between adjacent entries in the list, and the final bin will group all ROH larger than the final entry in the list. (list)
  • realSFS: Settings for realSFS
    • fold: Whether or not to fold the produced SFS. Set to 1 if you have not provided an ancestral-state reference (0 or 1, docs)
    • sfsboot: Determines number of bootstrap replicates to use when requesting bootstrapped SFS. Is used for both 1dsfs and 2dsfs (this is very easy to separate, open an issue if desired). Automatically used for heterozygosity analysis to calculate confidence intervals. (integer)
  • fst: Settings for $F_{ST}$ calculation in ANGSD
    • whichFst: Determines which $F_{ST}$ estimator is used by ANGSD. With 0 being the default Reynolds 1983 and 1 being the Bhatia 2013 estimator. The latter is preferable for small or uneven sample sizes (0 or 1, docs)
    • win_size: Window size in bp for sliding window analysis (integer)
    • win_step: Window step size in bp for sliding window analysis (integer)
  • thetas: Settings for pi, theta, and Tajima's D estimation
    • win_size: Window size in bp for sliding window analysis (integer)
    • win_step: Window step size in bp for sliding window analysis (integer)
    • minsites: Minimum sites to include window in report plot. This does not remove them from the actual output, just the report plot.
  • ngsadmix: Settings for admixture analysis with NGSadmix. This analysis is performed for a set of K groupings, and each K has several replicates performed. Replicates will continue until a set of N highest likelihood replicates converge, or the number of replicates reaches an upper threshold set here. Defaults for reps, minreps, thresh, and conv can be left as default for most.
    • kvalues: A list of values of K to fit the data to (list of integers)
    • reps: The maximum number of replicates to perform per K. Default is 100. (integer)
    • minreps: The minimum number of replicates to perform, even if replicates have converged. Default is 20. (integer)
    • thresh: The convergence threshold - the top replicates must all be within this value of log-likelihood units to consider the run converged. Default is 2. (integer)
    • conv: The number of top replicates to include in convergence assessment. Default is 3. (integer)
    • extra: Additional arguments to pass to NGSadmix (for instance, increasing -maxiter). (string, docs)
  • ibs: Settings for identity by state calculation with ANGSD
    • -doIBS: Whether to use a random (1) or consensus (2) base in IBS distance calculation (docs)